Download e-book for kindle: Artificial Neural Networks in Cancer Diagnosis, Prognosis, by R. N. G. Naguib, G. V. Sherbet

By R. N. G. Naguib, G. V. Sherbet

ISBN-10: 0849396921

ISBN-13: 9780849396922

The aptitude price of synthetic neural networks (ANN) as a predictor of malignancy has all started to obtain elevated reputation. study and case stories are available scattered all through a large number of journals. man made Neural Networks in melanoma prognosis, diagnosis, and sufferer administration brings jointly the paintings of most sensible researchers - essentially clinicians - who current the result of their state of the art paintings with ANNs as utilized to just about all significant components of melanoma for prognosis, analysis, and administration of the disease.The e-book introduces the idea of neural networks and the strategy in their software in oncology. it's not an workout in ANN study, however the presentation of a brand new approach for diagnosing and picking the remedy of cancers. The authors have incorporated just about all cancers for which there exist ANN purposes. whilst the information to be had is ill-defined and the advance of an algorithmic resolution tough, neural networks offer a non-linear strategy which is helping sift throughout the maze of knowledge and arrive at an inexpensive solution.Highly interdisciplinary in nature, this publication offers entire assurance of crucial fabrics on the subject of the purposes of ANNs within the melanoma box. With contributions from trendy learn facilities around the globe, it serves as an creation to how neural networks can be utilized for actual prediction or analysis and indicates why neural networks are extra exact. man made Neural Networks in melanoma prognosis, diagnosis, and sufferer administration offers an knowing of this new device, its purposes, and whilst it may be used.

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22. , 43, 2985-2990, 1983. 23. , Prognostic value of estrogen and progesterone receptors in operable breast cancer — results of a univariate and multivariate analysis, Cancer, 62, 2517-2524, 1988. 24. , Progesterone receptors as a prognostic factor in stage II breast cancer, N. Engl. J. , 309, 13431347, 1983. 25. , 47, 6126-6133, 1987. Molecular Prognostic Factors in Breast Cancer by ANNs 19 26. , Cathepsin D and breast cancer, Eur. J. Cancer, 32A, 15-24, 1996. 27. , Prognostic significance of the estrogenregulated protein, cathepsin D, in breast cancer, Cancer, 65, 265-271, 1990.

2 Expression of h-mts1 and nm23 genes and their individual relationship to nodal spread of breast cancer. 0) Gene expression Data from Albertazzi et al. [76]. s. ≡ not statistically significant. s. 16 ANNs in Cancer Diagnosis, Prognosis, and Patient Management B. h-mts1 EXPRESSION AND ER/PgR STATUS Among other markers used by Albertazzi et al. is the ER/PgR status of breast cancer. ER/PgR positivity generally correlates inversely with epidermal growth factor receptor (EGFr) status, which has been regarded by some as an indicator of poor prognosis [50].

This finding suggests that h-mts1 is an important factor in the analysis, and its omission significantly reduces the prediction statistics: notably, sensitivity is reduced by 50%. 3 . While the omission of nm23 has a noticeable negative effect on the prediction statistics, this effect is not as pronounced as in case (2) above for h-mts1. This fact then suggests that nm23 is an important factor that should not be neglected from the prediction analysis, but that its correlation with nodal involvement is perhaps not as prominent as that of h-mts1.

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Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management (Biomedical Engineering) by R. N. G. Naguib, G. V. Sherbet

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